Growth patterns of liver metastases as compared to resected tumors of the same colorectal cancer patients.
Bibliographic record
Abstract
519 Background: Colorectal cancer (CRC) affects one million people a year. 25% of the patients have metastatic disease at the time of diagnosis. Metastatic disease is one of the major challenges in the treatment of cancer. Even though the TNM staging system is a good prognostic marker, tumours have a biological diversity that cannot be evaluated by the TNM system. Recently three different growth patterns of CRC liver metastases were identified. The aim of this study was to determine the growth patterns of CRC liver metastases from patients with more than one metastasis. Methods: A pilot study was conducted including 34 patients resected for CRC liver metastases, 28 patients selected from a database of 200 patients, operated in Copenhagen between 2007-2010 and 6 patients, operated in Montreal. From these 34 patients, 15 patients were resected for two or more liver metastases. All paraffin embedded tissue sections were stained for reticulin and haematoxylin and eosin at the Finsen Laboratory. Evaluation of the growth pattern was done by three different observers from Antwerp and Copenhagen. Reproducibility was >90%. Results: In the fifteen patients, 9 from Copenhagen and 6 from Montreal, that had multiple hepatic metastases, there was uniformity in the growth pattern of the individual metastases. 40% had a desmoplastic, 33% a pushing and 20% a replacement growth pattern and 7% had a mix pattern. Of the 9 patients from Copenhagen 33% were synchronous and 67% metachronous metastases. Within patients, all patterns were identical. Conclusions: The uniformity of growth patterns seen in each patient suggests that these patterns are not random. The identical growth pattern may be determined by specific interactions between the tumor and the host microenvironment, as postulated by Paget’s seed and soil theory. This could represent three different stromal and cytokine responses or oncogenic pathway responses and could represent metastatic tumour growth in different microenvironments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".